- Long-term forecasts span 1-5 years and are used for strategic decisions like new product development. Medium-term forecasts are for 1 year and are used tactically for production and inventory plans. Short-term forecasts are for days to weeks and are more accurate. - Demand can be forecasted qualitatively using surveys or quantitatively using time series analysis of historical demand data. Causal methods link demand to economic indicators while time series extrapolation assumes past demand patterns continue. - Elementary techniques simply use last period's demand as the forecast. Moving averages use the average of the last few periods as the forecast. Exponential smoothing weights older data less than recent data in calculating forecasts.

1. Supply Chain : Logistics
Week 3
Forecasting Logistics Requirement

2. Introduction
• Forecasting is an attempt to determine in
advance the most likely outcome of an
uncertain variable.
• Planning and controlling logistics systems need
predictions for the level of future economic
activities because of the time lag in matching
supply to demand.
• Logistics requirements to be predicted include
customer demand, raw material prices, labour
costs and lead times

3. Period Time Forecast
Demand forecasts are organized by periods of
time into three general categories.
• Long-term forecasts.
– span a time horizon from one to five years.
– Predictions for longer periods are very unreliable,
since political and technological issues come into
play,
– used for deciding whether a new item should be put
on the market, or whether an old one should be
withdrawn

4. Period Time Forecast
• Medium-term forecasts.
– forecasts extend over a period from a few
months to one year.
– used for tactical logistical decisions, such as
setting annual production and distribution plans,
inventory management and slot allocation in
warehouses.

5. Period Time Forecast
• Short-term forecasts.
– cover a time interval from a few days to several
weeks.
– forecasts for a shorter time interval (a few hours
or a single day) are quite uncommon.
– Short-term forecasts are as a rule more accurate
than those for medium and long time periods.

6. Demand Forecasting
Method
Forecasting approaches can be classified in two
main categories:
• Qualitative Method.
– mainly based on workforce experience or on
surveys,
– usually employed for long- and medium-term
forecasts,when there is insufficient history to use a
quantitative approach.
– The most widely used qualitative methods are sales
force assessment, market research and the Delphi
method.

7. Demand Forecasting
Method
• Quantitative methods.
– can be used every time there is sufficient demand
history.
– Such techniques belong to two main groups:
• causal methods
based on the hypothesis that future demand depends on
the past or current values of some variables
• time series extrapolation.
some features of the past demand time pattern will
remain the same. The demand pattern is then projected in
the future.

8. Causal Method
• exploit the strong correlation between the
future demand of some items (or services) and
the past (or current) values of some causal
variables.
• For example,
– the demand for economy cars depends on the level
of economic activity and, therefore, can be related
to the GDP.
– the demand for spare parts can be associated with
the number of installed devices using them

9. Time Series Extrapolation
Method
• assume that the main features of past
demand pattern will be replicated in the
future.
• A forecast is then obtained by extrapolating
(projecting) the demand pattern.
• Such techniques are suitable for short- and
medium term predictions, where the
probability of a changeovers is low.

10. Elementary Technique
• The forecast for the first time period ahead is simply given
by
𝑝𝑇+1 = 𝑑𝑇
• Example
Sarath is a Malaysia-based distributor of Korean appliances.
The sales volume of portable TV sets during the last 12
weeks in Kuala Lumpur
The demand pattern is depicted in Figure below. It can be
seen that the trend is constant. By using the elementary
technique, we obtain
𝑝13 = 𝑑12 = 1177

11. Perioda
Waktu
Kuantitas
Perioda
Waktu
Kuantitas
1 1180 7 1162
2 1176 8 1163
3 1185 9 1180
4 1163 10 1170
5 1188 11 1161
6 1172 12 1177

13. Moving Average method
• The moving average method uses the
average of the r most recent demand entries
as the forecast for first period ahead (r ≥ 1):
• If r is chosen equal to 1, the moving average
method reduces to the elementary
technique.
𝑝𝑇+1 =
𝑘=0
𝑟−1
𝑑𝑇−𝑘
𝑟

14. Exponential Smoothing
Method
• The exponential smoothing method (also known as the
Brown method).
• as an evolution over the moving average technique.
• The demand forecast is obtained by taking into account
all historical data and assigning lower weights to older
data.
• The demand forecast for the first period ahead is given by
𝑝𝑇+1 = 𝛼𝑑𝑇 + 1 − 𝛼 𝑝𝑇

15. Thank You